کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5709483 1604180 2017 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Validation of a personalized curved muscle model of the lumbar spine during complex dynamic exertions
ترجمه فارسی عنوان
اعتبار یک مدل عضلانی منحنی فردی ستون فقرات کمری در طول اعمال پیچیده دینامیکی
موضوعات مرتبط
علوم پزشکی و سلامت پزشکی و دندانپزشکی ارتوپدی، پزشکی ورزشی و توانبخشی
چکیده انگلیسی
Previous curved muscle models have typically examined their robustness only under simple, single-plane static exertions. In addition, the empirical validation of curved muscle models through an entire lumbar spine has not been fully realized. The objective of this study was to empirically validate a personalized biologically-assisted curved muscle model during complex dynamic exertions. Twelve subjects performed a variety of complex lifting tasks as a function of load weight, load origin, and load height. Both a personalized curved muscle model as well as a straight-line muscle model were used to evaluate the model's fidelity and prediction of three-dimensional spine tissue loads under different lifting conditions. The curved muscle model showed better model performance and different spinal loading patterns through an entire lumbar spine compared to the straight-line muscle model. The curved muscle model generally showed good fidelity regardless of lifting condition. The majority of the 600 lifting tasks resulted in a coefficient of determination (R2) greater than 0.8 with an average of 0.83, and the average absolute error less than 15% between measured and predicted dynamic spinal moments. As expected, increased load and asymmetry were generally found to significantly increase spinal loads, demonstrating the ability of the model to differentiate between experimental conditions. A curved muscle model would be useful to estimate precise spine tissue loads under realistic circumstances. This precise assessment tool could aid in understanding biomechanical causal pathways for low back pain.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Electromyography and Kinesiology - Volume 33, April 2017, Pages 1-9
نویسندگان
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